{"id":"W4390874202","doi":"10.1109/iccv51070.2023.01639","title":"LDL: Line Distance Functions for Panoramic Localization","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Research Foundation","keywords":"Computer science; Computer vision; Artificial intelligence; Line (geometry); Panorama; Computation; Feature (linguistics); Matching (statistics); Line segment; Pipeline (software); Point (geometry); Complement (music); Distance transform; Pattern recognition (psychology); Algorithm; Image (mathematics); Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005317489,0.001303004,0.0007765996,0.001828637,0.0005258517,0.001553265,0.001898147,0.001119046,0.01529374],"category_scores_gemma":[0.002514772,0.0005579707,0.000737339,0.001584696,0.0006377414,0.001853311,0.00236849,0.001698357,0.0117681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007410569,"about_ca_system_score_gemma":0.0007776053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002510794,"about_ca_topic_score_gemma":0.003293084,"domain_scores_codex":[0.9993413,0.00008105995,0.00003099495,0.0001861463,0.0002869887,0.00007355557],"domain_scores_gemma":[0.9993795,0.0001183831,0.00007801038,0.0001711826,0.0001985656,0.00005430247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001978303,0.00007867772,0.0009120337,0.0002818342,0.00006328133,0.0001313187,0.0002038558,0.0339505,0.03524286,0.0190958,0.02912004,0.880722],"study_design_scores_gemma":[0.00008978173,0.0001620014,0.001583448,0.0001051458,0.00003773661,0.0007197818,0.0002218003,0.7793785,0.06484976,0.03810807,0.1146144,0.0001295759],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001367375,0.0001117027,0.9907197,0.00004589951,0.00003098539,0.00002924749,0.000214256,0.006497254,0.000983516],"genre_scores_gemma":[0.07364722,0.0003078132,0.9160577,0.0001905324,0.00008036796,0.0002470946,0.002192524,0.002492412,0.004784267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01529374,"threshold_uncertainty_score":0.05116266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01654758300205196,"score_gpt":0.225970340759013,"score_spread":0.209422757756961,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}